Application of an Isolated Word Speech Recognition System in the Field of Mental Health Consultation: Development and Usability Study

Background

Speech recognition is a technology that enables machines to understand human language.

Objective

In this study, speech recognition of isolated words from a small vocabulary was applied to the field of mental health counseling.

Methods

A software platform was used to establish a human-machine chat for psychological counselling. The software uses voice recognition technology to decode the user's voice information. The software system analyzes and processes the user's voice information according to many internal related databases, and then gives the user accurate feedback. For users who need psychological treatment, the system provides them with psychological education.

Results

The speech recognition system included features such as speech extraction, endpoint detection, feature value extraction, training data, and speech recognition.

Conclusions

The Hidden Markov Model was adopted, based on multithread programming under a VC2005 compilation environment, to realize the parallel operation of the algorithm and improve the efficiency of speech recognition. After the design was completed, simulation debugging was performed in the laboratory. The experimental results showed that the designed program met the basic requirements of a speech recognition system.

The longevity of statistical learning: When infant memory decays, isolated words come to the rescue

Research over the past 2 decades has demonstrated that infants are equipped with remarkable computational abilities that allow them to find words in continuous speech. Infants can encode information about the transitional probability (TP) between syllables to segment words from artificial and natural languages. As previous research has tested infants immediately after familiarization, infants' ability to retain sequential statistics beyond the immediate familiarization context remains unknown. Here, we examine infants' memory for statistically defined words 10 min after familiarization with an Italian corpus. Eight-month-old English-learning infants were familiarized with Italian sentences that contained 4 embedded target words-2 words had high internal TP (HTP, TP = 1.0) and 2 had low TP (LTP, TP = .33)-and were tested on their ability to discriminate HTP from LTP words using the Headturn Preference Procedure. When tested after a 10-min delay, infants failed to discriminate HTP from LTP words, suggesting that memory for statistical information likely decays over even short delays (Experiment 1). Experiments 2-4 were designed to test whether experience with isolated words selectively reinforces memory for statistically defined (i.e., HTP) words. When 8-month-olds were given additional experience with isolated tokens of both HTP and LTP words immediately after familiarization, they looked significantly longer on HTP than LTP test trials 10 min later. Although initial representations of statistically defined words may be fragile, our results suggest that experience with isolated words may reinforce the output of statistical learning by helping infants create more robust memories for words with strong versus weak co-occurrence statistics. (PsycINFO Database Record

Word segmentation from noise-band vocoded speech

Spectral degradation reduces access to the acoustics of spoken language and compromises how learners break into its structure. We hypothesised that spectral degradation disrupts word segmentation, but that listeners can exploit other cues to restore detection of words. Normal-hearing adults were familiarised to artificial speech that was unprocessed or spectrally degraded by noise-band vocoding into 16 or 8 spectral channels. The monotonic speech stream was pause-free (Experiment 1), interspersed with isolated words (Experiment 2), or slowed by 33% (Experiment 3). Participants were tested on segmentation of familiar vs. novel syllable sequences and on recognition of individual syllables. As expected, vocoding hindered both word segmentation and syllable recognition. The addition of isolated words, but not slowed speech, improved segmentation. We conclude that syllable recognition is necessary but not sufficient for successful word segmentation, and that isolated words can facilitate listeners' access to the structure of acoustically degraded speech.